基于多个 LLM 代理的经济和公共政策分析框架

<p/>本文开创了一种利用多个大语言模型(LLMs)作为异质性代理人的经济和公共政策分析新方法.我们首先在两种不同场景下评估了主流LLMs解决两期消费分配问题的经济决策能力:一种基于显式效用函数,另一种依靠直觉推理.不同于先前仅通过调整提示词模拟代理异质性的研究,我们创新性地利用不同LLMs之间固有的分析能力差异来模拟具有不同认知特征的经济主体.基于这些发现,我们构建了基于多个LLM代理的(multi-LLM-agent-based, MLAB)框架,将各种LLMs映射到特定教育群体及其对应收入阶层.通过利息收入税政策案例研究,我们展示了MLAB框架如何模拟政策对不同群体的差异化影响,为经济和公共政策分析开辟了富有前景的新路径.<br/> This paper pioneers a novel approach to economic and public policy analysis by leveraging multiple large language models (LLMs) as heterogeneous artificial economic agents. We first evaluate five LLMs’ economic decision-making capabilities in solving two-period consumption allocation problems under two distinct scenarios: With explicit utility functions and based on intuitive reasoning. While previous research has often simulated heterogeneity by solely varying prompts, our approach harnesses the inherent variations in analytical capabilities across different LLMs to model agents with diverse cognitive traits. Building on these findings, we construct a multi-LLM-agent-based (MLAB) framework by mapping these LLMs to specific educational groups and corresponding income brackets. Using interest income taxation as a case study, we demonstrate how the MLAB framework can simulate policy impacts across heterogeneous agents, offering a promising new direction for economic and public policy analysis by leveraging LLMs’ human-like reasoning capabilities and computational power.